Krishna Sai Vootla, Machine Learning Developer in Bangalore, Karnataka, India
Krishna Sai Vootla

Machine Learning Developer in Bangalore, Karnataka, India

Member since October 31, 2019
Krishna is a machine learning engineer who is curious and passionate about applied deep learning in computer vision, NLP, and reinforcement learning. He has four years of experience with machine learning, including having been a part of the analytics division of JP Morgan Chase & Co. He is a great communicator and enthusiastic developer.
Krishna is now available for hire

Portfolio

Experience

Location

Bangalore, Karnataka, India

Availability

Part-time

Preferred Environment

PyCharm, Tableau, RStudio, Spyder, Jupyter Notebook, SQL, Machine Learning, Python

The most amazing...

...achievement of mine is winning third prize globally in Intel ESDC competition held in Shanghai, China.

Employment

  • Data Scientist

    2020 - PRESENT
    Organifi (via Toptal)
    • Developed and deployed an NLP pipeline to extract and summarize opinions and feedback from customer product reviews.
    • Built data pipelines in AWS and GCP for a reporting and analytics data warehouse.
    • Built numerous executive summary dashboards in Tableau by identifying key metrics to track goals specific to individual teams.
    Technologies: Python 3, Natural Language Processing (NLP), Sentiment Analysis, Data Analytics, BigQuery, Google Cloud Functions, Google Cloud, Tableau, Tableau Desktop Pro, MySQL, AWS, AWS Lambda, AWS RDS, Data Science, Technical Hiring, Code Review, Interviewing, Source Code Review, SQL, Machine Learning, Python, Deep Learning
  • Analyst

    2019 - 2019
    JP Morgan Chase & Co
    • Designed and built a next-generation merchant acquisition tool in R Shiny for a credit card business.
    • Provided pricing analysis of credit card business.
    • Built and integrated a minimum revenue model based on customer demographics.
    Technologies: Tableau, Python, R, Code Review, Source Code Review, SQL, Machine Learning
  • Business Analyst

    2018 - 2019
    Tredence Analytics
    • Segmented retail customers based on their shopping behavior by using random forest.
    • Designed, built, and deployed an end-to-end machine learning pipeline.
    • Performed marketing analysis of a leading retail company in the US.
    Technologies: Tableau, R, Python, Interviewing, Source Code Review, Task Analysis, Data Science, SQL, Machine Learning
  • Software Analyst

    2017 - 2018
    Capgemini
    • Scraped the web for collecting unstructured data present on a website.
    • Created and deployed various executive summary dashboards.
    • Automated data cleaning pipelines to save significant person-hours every week.
    Technologies: Python, MySQL, Linux, Task Analysis

Experience

  • Multi-modal Fully Convolutional Network for Semantic Segmentation
    https://github.com/prml615/prml

    A fully convolutional network (FCN-32s) trained to semantically segment forest scene images with RGB and nir_color input images.

    The project was developed to help unmanned drones in smooth navigation. The model is trained and tested on still images of forest scenes.

    I used Intel Edison and Microsoft Kinect for proof of concept and prototype creation.

  • Smart Medical Network

    I worked on a smart medical network for Intel ESDC 2016, Shanghai. The project aimed to create an ecosystem of a medical network that stores the clinical and real-time data of patients for smoother and quicker diagnosis in an emergency.

Skills

  • Languages

    SQL, Python, Python 3, R, C++, C, Embedded C
  • Paradigms

    Data Science
  • Other

    Freelance, Machine Learning, Technical Hiring, Code Review, Source Code Review, Algorithms, Neural Networks, Deep Neural Networks, Deep Learning, Computer Vision, Natural Language Processing (NLP), Data Analytics, Data Reporting, Exploratory Data Analysis, Statistical Data Analysis, Statistical Learning, Statistical Modeling, Analytics, Predictive Analytics, Statistical Analysis, Data Analysis, Artificial Intelligence (AI), Artificial Neural Networks (ANN), Interviewing, Task Analysis, Quantitative Analysis, Sentiment Analysis, Google Cloud Functions, AWS, AWS RDS
  • Frameworks

    RStudio Shiny, Microsoft Kinect
  • Libraries/APIs

    Keras, NumPy, Pandas, Matplotlib, Ggplot2, Scikit-learn, Tidyverse, Beautiful Soup, Standard Template Library (STL), SciPy, OpenCV, TensorFlow
  • Tools

    Tableau, Dplyr, Scikit-image, Looker, PyCharm, Jupyter, Spyder, BigQuery, Tableau Desktop Pro
  • Storage

    MySQL, Google Cloud, Google Cloud Storage
  • Platforms

    RStudio, Linux, Oracle, Arduino, Raspberry Pi, Raspberry Pi 3 GPIO, Jupyter Notebook, Anaconda, AWS Lambda

Education

  • Bachelor of Technology Degree in Electrical Engineering
    2013 - 2017
    Indian Institute of Technology Gandhinagar - Gandhinagar, India

Certifications

  • Statistical Learning
    MARCH 2020 - PRESENT
    Stanford Online
  • Sentiment Analysis in Python
    OCTOBER 2019 - PRESENT
    DataCamp
  • Building Web Applications in R with Shiny: Case Studies
    MARCH 2019 - PRESENT
    DataCamp
  • Building Web Applications in R with Shiny
    MARCH 2019 - PRESENT
    DataCamp
  • CodeChef Certified Data Structure & Algorithms Programme
    OCTOBER 2018 - PRESENT
    CodeChef
  • Intermediate R
    SEPTEMBER 2018 - PRESENT
    DataCamp
  • Data Manipulation in R with dplyr
    AUGUST 2018 - PRESENT
    DataCamp
  • Introduction to R
    JULY 2018 - PRESENT
    DataCamp
  • Python A-Z: Python for Data Science with Real Exercises!
    MARCH 2018 - PRESENT
    Udemy
  • SQL - MySQL for Data Analytics & Business Intelligence
    MARCH 2018 - PRESENT
    Udemy
  • Structuring Machine Learning Projects
    FEBRUARY 2018 - PRESENT
    Coursera

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